Post-insufflation diaphragm contractions in patients receiving various modes of mechanical ventilation.

BACKGROUND: During mechanical ventilation, post-insufflation diaphragm contractions (PIDCs) are non-physiologic and could be injurious. PIDCs could be frequent during reverse-triggering, where diaphragm contractions follow the ventilator rhythm. Whether PIDCs happens with different modes of assisted ventilation is unknown. In mechanically ventilated patients with hypoxemic respiratory failure, we aimed to examine whether PIDCs are associated with ventilator settings, patients' characteristics or both. METHODS: One-hour recordings of diaphragm electromyography (EAdi), airway pressure and flow were collected once per day for up to five days from intubation until full recovery of diaphragm activity or death. Each breath was classified as mandatory (without-reverse-triggering), reverse-triggering, or patient triggered. Reverse triggering was further subclassified according to EAdi timing relative to ventilator cycle or reverse triggering leading to breath-stacking. EAdi timing (onset, offset), peak and neural inspiratory time (Tineuro) were measured breath-by-breath and compared to the ventilator expiratory time. A multivariable logistic regression model was used to investigate factors independently associated with PIDCs, including EAdi timing, amplitude, Tineuro, ventilator settings and APACHE II. RESULTS: Forty-seven patients (median[25%-75%IQR] age: 63[52-77] years, BMI: 24.9[22.9-33.7] kg/m2, 49% male, APACHE II: 21[19-28]) contributed 2 ± 1 recordings each, totaling 183,962 breaths. PIDCs occurred in 74% of reverse-triggering, 27% of pressure support breaths, 21% of assist-control breaths, 5% of Neurally Adjusted Ventilatory Assist (NAVA) breaths. PIDCs were associated with higher EAdi peak (odds ratio [OR][95%CI] 1.01[1.01;1.01], longer Tineuro (OR 37.59[34.50;40.98]), shorter ventilator inspiratory time (OR 0.27[0.24;0.30]), high peak inspiratory flow (OR 0.22[0.20;0.26]), and small tidal volumes (OR 0.31[0.25;0.37]) (all P ≤ 0.008). NAVA was associated with absence of PIDCs (OR 0.03[0.02;0.03]; P < 0.001). Reverse triggering was characterized by lower EAdi peak than breaths triggered under pressure support and associated with small tidal volume and shorter set inspiratory time than breaths triggered under assist-control (all P < 0.05). Reverse triggering leading to breath stacking was characterized by higher peak EAdi and longer Tineuro and associated with small tidal volumes compared to all other reverse-triggering phenotypes (all P < 0.05). CONCLUSIONS: In critically ill mechanically ventilated patients, PIDCs and reverse triggering phenotypes were associated with potentially modifiable factors, including ventilator settings. Proportional modes like NAVA represent a solution abolishing PIDCs.

Knowledge and associated factors of healthcare professionals in detecting patient-ventilator asynchrony using waveform analysis at intensive care units of the federal public hospitals in Addis Ababa, Ethiopia, 2023.

BACKGROUND: The interaction between the patient and the ventilator is often disturbed, resulting in patient-ventilator asynchrony (PVA). Asynchrony can lead to respiratory failure, increased artificial ventilation time, prolonged hospitalization, and escalated healthcare costs. Professionals' knowledge regarding waveform analysis has significant implications for improving patient outcomes and minimizing ventilation-related adverse events. Studies investigating the knowledge of healthcare professionals on patient-ventilator asynchrony and its associated factors in the Ethiopian context are limited. Therefore, this study aimed to assess the knowledge of healthcare professionals about using waveform analysis to detect asynchrony. METHODS: A multicenter cross-sectional study was conducted on 237 healthcare professionals (HCPs) working in the intensive care units (ICUs) of federal public hospitals in Addis Ababa, Ethiopia, from December 2022 to May 2023. The data were collected using a structured and pretested interviewer-administered questionnaire. Then, the collected data were cleaned, coded, and entered into Epi data V-4.2.2 and exported to SPSS V-27 for analysis. After description, associations were analyzed using binary logistic regression. Variables with a P-value of < 0.25 in the bivariable analysis were transferred to the multivariable analysis. Statistical significance was declared using 95% confidence intervals, and the strengths of associations were reported using adjusted odds ratios (AORs). RESULTS: A total of 237 HCPs participated in the study with a response rate of 100%. Half (49.8%) of the participants were females. The mean age of the participants was 29 years (SD = 3.57). Overall, 10.5% (95% CI: 6.9-15.2) of the participants had good knowledge of detecting PVA using waveform analysis. In the logistic regression, the number of MV-specific trainings and the training site had a statistically significant association with knowledge of HCPs. HCPs who attended more frequent MV training were more likely to have good knowledge than their counterparts [AOR = 6.88 (95% CI: 2.61-15.45)]. Additionally, the odds of good knowledge among professionals who attended offsite training were 2.6 times higher than those among professionals trained onsite [AOR = 2.63 (95% CI: 1.36-7.98)]. CONCLUSION: The knowledge of ICU healthcare professionals about the identification of PVA using waveform analysis is low. In addition, the study also showed that attending offsite MV training and repeated MV training sessions were independently associated with good knowledge. Consequently, the study findings magnify the relevance of providing frequent and specific training sessions focused on waveform analysis to boost the knowledge of HCPs.

[A Review on Automatic Detection Algorithm for Patient-Ventilator Asynchrony during Mechanical Ventilation].

This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony (PVA) during mechanical ventilation. In the early stages, the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms. While these methods were intuitive and easy to operate, they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status. Subsequently, machine learning and deep learning technologies began to emerge and develop. These technologies automatically extract and learn data characteristics through algorithms, making PVA detection more robust and universal. Among them, logistic regression, support vector machines, random forest, hidden Markov models, convolutional autoencoders, long short-term memory networks, one-dimensional convolutional neural networks, etc., have all been successfully used for PVA recognition. Despite the significant advancements in feature extraction through deep learning methods, their demand for labelled data is high, potentially consuming significant medical resources. Therefore, the combination of reinforcement learning and self-supervised learning may be a viable solution. In addition, most algorithm validations are based on a single dataset, so the need for cross-dataset validation in the future will be an important and challenging direction for development.

Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses.

BACKGROUND: Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop. METHODS: Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors. RESULTS: The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment. CONCLUSIONS: The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.

A novel technique for assessment of post-extubation airway obstruction can successfully replace the conventional cuff leak test: a pilot study.

BACKGROUND: Post-extubation airway obstruction is an important complication of tracheal intubation. The cuff leak test is traditionally used to estimate the risk of this complication. However, the cuff leak test parameters are not constant and may depend on the respiratory system and ventilator settings. Furthermore, deflating the cuff also be a risk factor for patient-ventilator asynchrony and ventilator-associated pneumonia. Instead of using the cuff leak test, we measured the pressure of the leak to the upper airway through the gap between the tube and glottis with a constant low flow from the lumen above the cuff without deflating the cuff and called it "pressure above the cuff." The purpose of this study was to investigate whether pressure above the cuff can be used as an alternative to the cuff leak volume. METHODS: This prospective observational study was conducted at Kumamoto University Hospital after obtaining approval from the institutional review board. The pressure above the cuff was measured using an endotracheal tube with an evacuation lumen above the cuff and an automated cuff pressure modulation device. We pumped 0.16 L per minute of air and measured the steady-state pressure using an automated cuff pressure modulation device. Then, the cuff leak test was performed, and the cuff leak volume was recorded. The cuff leak volume was defined as the difference between the expiratory tidal volume with the cuff inflated and deflated. The relationship between the pressure above the cuff and cuff leak volume was evaluated. The patient-ventilator asynchrony during each measurement was also examined. RESULTS: The pressure above the cuff was measured, and the cuff leak volume was assessed 27 times. The pressure above the cuff was significantly correlated with the cuff leak volume (r = -0.76, p < 0.001). Patient-ventilator asynchrony was detected in 37% of measurements during the cuff leak test, but not during the pressure above the cuff test. CONCLUSIONS: This study suggests that pressure above the cuff measurement may be a less complicated alternative to the conventional cuff leak test for evaluation of the risk of post-extubation airway obstruction. TRIAL REGISTRATION: University Hospital Medical Information Network Clinical Trials Registry (UMIN000039987; March 30, 2020). https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000044604.

Bygone Ether: Theriac to Obstinate Hiccups-Food for Thought!

How to cite this article: Hirolli D, Panda R, Baidya DK. Bygone Ether: Theriac to Obstinate Hiccups-Food for Thought! Indian J Crit Care Med 2022;26(7):884.

Neurally adjusted ventilatory assist as a weaning mode for adults with invasive mechanical ventilation: a systematic review and meta-analysis.

BACKGROUND: Prolonged ventilatory support is associated with poor clinical outcomes. Partial support modes, especially pressure support ventilation, are frequently used in clinical practice but are associated with patient-ventilation asynchrony and deliver fixed levels of assist. Neurally adjusted ventilatory assist (NAVA), a mode of partial ventilatory assist that reduces patient-ventilator asynchrony, may be an alternative for weaning. However, the effects of NAVA on weaning outcomes in clinical practice are unclear. METHODS: We searched PubMed, Embase, Medline, and Cochrane Library from 2007 to December 2020. Randomized controlled trials and crossover trials that compared NAVA and other modes were identified in this study. The primary outcome was weaning success which was defined as the absence of ventilatory support for more than 48 h. Summary estimates of effect using odds ratio (OR) for dichotomous outcomes and mean difference (MD) for continuous outcomes with accompanying 95% confidence interval (CI) were expressed. RESULTS: Seven studies (n = 693 patients) were included. Regarding the primary outcome, patients weaned with NAVA had a higher success rate compared with other partial support modes (OR = 1.93; 95% CI 1.12 to 3.32; P = 0.02). For the secondary outcomes, NAVA may reduce duration of mechanical ventilation (MD = - 2.63; 95% CI - 4.22 to - 1.03; P = 0.001) and hospital mortality (OR = 0.58; 95% CI 0.40 to 0.84; P = 0.004) and prolongs ventilator-free days (MD = 3.48; 95% CI 0.97 to 6.00; P = 0.007) when compared with other modes. CONCLUSIONS: Our study suggests that the NAVA mode may improve the rate of weaning success compared with other partial support modes for difficult to wean patients.

Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning.

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

Expiratory obstruction in patients with Duchenne muscular dystrophy under non-invasive ventilation: A step-by-step analysis of a new obstructive pattern.

PURPOSE: Non-invasive ventilation (NIV) is the reference standard for managing chronic hypoventilation in patients with Duchenne muscular dystrophy (DMD). In these patients, upper airway obstruction under NIV may compromise efficacy and adherence. We aim to describe a novel pattern of expiratory obstructive events occurring during nocturnal barometric NIV. METHODS: We retrospectively included all patients with DMD who underwent full-night polygraphy during NIV as part of their usual follow-up between May 2018 and July 2019. RESULTS: We provide a step-by-step description of this previously undescribed pattern of obstruction. Expiratory obstructions lead to end-inspiratory breath-holding and impossibility to take another inspiratory breath with a barometric mode until expiration occurs. These events were observed in 4 (36%) of 11 DMD patients under barometric NIV. CONCLUSION: Expiratory obstructions may be common in DMD patients receiving NIV and should be sought out routinely. This previously undescribed variant of obstructive event must be identified.

Managing patient-ventilator asynchrony with a twice-daily screening protocol: A retrospective cohort study.

PURPOSE: Severe patient-ventilator asynchrony (PVA) might be associated with prolonged mechanical ventilation and mortality. It is unknown if systematic screening and application of conventional methods for PVA management can modify these outcomes. We therefore constructed a twice-daily bedside PVA screening and management protocol and investigated its effect on patient outcomes. MATERIALS AND METHODS: A retrospective cohort study of patients who were intubated in the emergency department and directly admitted to the medical intensive care unit (ICU). In phase 1 (6 months; August 2016 to January 2017), patients received usual care comprising lung protective ventilation and moderate analgesia/sedation. In phase 2 (6 months; February 2017 to July 2017), patients were additionally managed with a PVA protocol on ICU admission and twice daily (7 am, 7 pm). RESULTS: A total of 280 patients (160 in phase 1, 120 in phase 2) were studied (age = 64.5 ± 21.4 years, 107 women [38.2%], Acute Physiology and Chronic Health Evaluation II score = 27.1 ± 8.5, 271 [96.8%] on volume assist-control ventilation initially). Phase 2 patients had lower hospital mortality than phase 1 patients (20.0% versus 34.4%, respectively, P = 0.011), even after adjustment for age and Acute Physiology and Chronic Health Evaluation II scores (odds ratio = 0.46, 95% confidence interval = 0.25-0.84). CONCLUSIONS: Application of a bedside PVA protocol for mechanically ventilated patients on ICU admission and twice daily was associated with decreased hospital mortality. There was however no association with sedation-free days or mechanical ventilation-free days through day 28 or length of hospital stay.

Additional work of breathing from trigger errors in mechanically ventilated children.

BACKGROUND: Patient-ventilator asynchrony is associated with increased morbidity and mortality. A direct causative relationship between Patient-ventilator asynchrony and adverse clinical outcome have yet to be demonstrated. It is hypothesized that during trigger errors excessive pleural pressure swings are generated, contributing to increased work-of-breathing and self-inflicted lung injury. The objective of this study was to determine the additional work-of-breathing and pleural pressure swings caused by trigger errors in mechanically ventilated children. METHODS: Prospective observational study in a tertiary paediatric intensive care unit in an university hospital. Patients ventilated > 24 h and < 18 years old were studied. Patients underwent a 5-min recording of the ventilator flow-time, pressure-time and oesophageal pressure-time scalar. Pressure-time-product calculations were made as a proxy for work-of-breathing. Oesophageal pressure swings, as a surrogate for pleural pressure swings, during trigger errors were determined. RESULTS: Nine-hundred-and-fifty-nine trigger errors in 28 patients were identified. The additional work-of-breathing caused by trigger errors showed great variability among patients. The more asynchronous breaths were present the higher the work-of-breathing of these breaths. A higher spontaneous breath rate led to a lower amount of trigger errors. Patient-ventilator asynchrony was not associated with prolonged duration of mechanical ventilation or paediatric intensive care stay. CONCLUSIONS: The additional work-of-breathing caused by trigger errors in ventilated children can take up to 30-40% of the total work-of-breathing. Trigger errors were less common in patients breathing spontaneously and those able to generate higher pressure-time-product and pressure swings. TRIAL REGISTRATION: Not applicable.

Clinical decision support recommending ventilator settings during noninvasive ventilation.

NIV therapy is used to provide positive pressure ventilation for patients. There are protocols describing what ventilator settings to use to initialize NIV; however, the guidelines for titrating ventilator settings are less specific. We developed an advisory system to recommend NIV ventilator setting titration and recorded respiratory therapist agreement rates at the bedside. We developed an algorithm (NIV advisor) to recommend when to change the non-invasive ventilator settings of IPAP, EPAP, and FiO2 based on patient respiratory parameters. The algorithm utilized a multi-target approach; oxygenation, ventilation, and patient effort. The NIV advisor recommended ventilator settings to move the patient's respiratory parameters in a preferred target range. We implemented a pilot study evaluating the usability of the NIV advisor on 10 patients receiving critical care with non-invasive ventilation (NIV). Respiratory therapists were asked their agreement on recommendations from the NIV advisor at the patient's bedside. Bedside respiratory therapists agreed with 91% of the ventilator setting recommendations from the NIV advisor. The POB and VT values were the respiratory parameters that were most often out of the preferred target range. The IPAP ventilator setting was the setting most often considered in need of changing by the NIV advisor. The respiratory therapists agreed with the majority of the recommendations from the NIV advisor. We consider the IPAP recommendations informative in providing the respiratory therapist assistance in targeting preferred POB and Vt values, as these values were frequently out of the target ranges. This pilot implementation was unable to produce the results required to determine the value of the EPAP recommendations. The FiO2 recommendations from the NIV advisor were treated as ancillary information behind the IPAP recommendations.

Validation of an automated system for detecting ineffective triggering asynchronies during mechanical ventilation: a retrospective study.

We compare the sensitivity and specificity of clinician visual waveform analysis against an automated system's waveform analysis in detecting ineffective triggering in mechanically ventilated intensive care unit patients when compared against a reference label set based upon analysis of respiratory muscle activity. Electrical activity of the diaphragm or esophageal/transdiaphragmatic pressure waveforms were available to a single clinician for the generation of a reference label set indicating the ground truth, that is, presence or absence of ineffective triggering, on a breath-by-breath basis. Pressure and flow versus time tracings were made available to (i) a group of three clinicians; and (ii) the automated Syncron-E™ system capable of detecting patient-ventilator asynchrony in real-time, in order to obtain breath-by-breath labels indicating the presence or absence of ineffective triggering. The clinicians and the automated system did not have access to other waveforms such as electrical activity of the diaphragm or esophageal/transdiaphragmatic pressure. In total, 926 breaths were analyzed across the seven patients. Specificity for clinicians and the automated system were high (99.3% for clinician and 98.5% for the automated system). The automated system had a significantly higher sensitivity (83.2%) compared to clinicians (41.1%). Ineffective triggering detected by the automated system, which has access only to airway pressure and flow versus time tracings, is in substantial agreement with a reference detection derived from analysis of invasively measured patient effort waveforms.

Flow and airway pressure analysis for detecting ineffective effort during mechanical ventilation in neuromuscular patients.

Ineffective efforts (IEs) are among the most common types of patient-ventilator asynchrony. The objective of this study is to validate IE detection during expiration using pressure and flow signals, with respiratory effort detection by esophageal pressure (Pes) measurement as the reference, in patients with neuromuscular diseases (NMDs). We included 10 patients diagnosed with chronic respiratory failure related to NMD. Twenty-eight 5-minute recordings of daytime ventilation were studied for IE detection. Standard formulas were used to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of IE detection using pressure and flow signals compared to Pes measurement. Mean sensitivity and specificity of flow and pressure signal-based IE detection versus Pes measurement were 97.5% ± 5.3% and 91.4% ± 13.7%, respectively. NPV was 98.1% ± 8.2% and PPV was 67.6% ± 33.8%. Spearman's rank correlation coefficient indicated a moderately significant correlation between frequencies of IEs and controlled cycles ( ρ = 0.50 and p = 0.01). Among respiratory cycles, 311 (11.2%) were false-positive IEs overall. Separating false-positive IEs according to their mechanisms, we observed premature cycling in 1.2% of cycles, delayed ventilator triggering in 0.1%, cardiac contraction in 9.2%, and upper airway instability during expiration in 0.3%. Using flow and pressure signals to detect IEs is a simple and rapid method that produces adequate data to support clinical decisions.

New setting of neurally adjusted ventilatory assist for noninvasive ventilation by facial mask: a physiologic study.

BACKGROUND: Noninvasive ventilation (NIV) is generally delivered using pneumatically-triggered and cycled-off pressure support (PSP) through a mask. Neurally adjusted ventilatory assist (NAVA) is the only ventilatory mode that uses a non-pneumatic signal, i.e., diaphragm electrical activity (EAdi), to trigger and drive ventilator assistance. A specific setting to generate neurally controlled pressure support (PSN) was recently proposed for delivering NIV by helmet. We compared PSN with PSP and NAVA during NIV using a facial mask, with respect to patient comfort, gas exchange, and patient-ventilator interaction and synchrony. METHODS: Three 30-minute trials of NIV were randomly delivered to 14 patients immediately after extubation to prevent post-extubation respiratory failure: (1) PSP, with an inspiratory support ≥8 cmH2O; (2) NAVA, adjusting the NAVA level to achieve a comparable peak EAdi (EAdipeak) as during PSP; and (3) PSN, setting the NAVA level at 15 cmH2O/µV with an upper airway pressure (Paw) limit to obtain the same overall Paw applied during PSP. We assessed patient comfort, peak inspiratory flow (PIF), time to reach PIF (PIFtime), EAdipeak, arterial blood gases, pressure-time product of the first 300 ms (PTP300-index) and 500 ms (PTP500-index) after initiation of patient effort, inspiratory trigger delay (DelayTR-insp), and rate of asynchrony, determined as asynchrony index (AI%). The categorical variables were compared using the McNemar test, and continuous variables by the Friedman test followed by the Wilcoxon test with Bonferroni correction for multiple comparisons (p < 0.017). RESULTS: PSN significantly improved patient comfort, compared to both PSP (p = 0.001) and NAVA (p = 0.002), without differences between the two latter (p = 0.08). PIF (p = 0.109), EAdipeak (p = 0.931) and gas exchange were similar between modes. Compared to PSP and NAVA, PSN reduced PIFtime (p < 0.001), and increased PTP300-index (p = 0.004) and PTP500-index (p = 0.001). NAVA and PSN significantly reduced DelayTR-insp, as opposed to PSP (p < 0.001). During both NAVA and PSN, AI% was <10% in all patients, while AI% was ≥10% in 7 patients (50%) with PSP (p = 0.023 compared with both NAVA and PSN). CONCLUSIONS: Compared to both PSP and NAVA, PSN improved comfort and patient-ventilator interaction during NIV by facial mask. PSN also improved synchrony, as opposed to PSP only. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03041402 . Registered (retrospectively) on 2 February 2017.

Combined effects of leaks, respiratory system properties and upper airway patency on the performance of home ventilators: a bench study.

BACKGROUND: Combined effects of leaks, mechanical property of respiratory system and upper airway (UA) patency on patient-ventilator synchrony (PVA) and the level of clinically "tolerable" leaks are not well established in home ventilators. METHODS: We comparatively assessed on a bench model, the highest leak level tolerated without inducing significant asynchrony ("critical leak") in three home ventilators (Astral 150, Trilogy 100 and Vivo 60; noted as A150, T100 and V60 respectively) subjected to three simulated diseased respiratory conditions: chronic obstructive pulmonary disease (COPD), obesity hypoventilation (OHS) and neuromuscular disorders (NMD), with both open and closed UA. Also, total leak values in the device reports were compared to the bench-measured values. RESULTS: With open UA, all ventilators were able to avoid asynchrony up to a 30 L/min leak and even to 55 L/min in some cases. UA closure and respiratory diseases especially OHS influenced PVA. With closed UA, the critical leak of A150 and T100 remained higher than 55 L/min in COPD and OHS, while for V60 decreased to 41 and 33 L/min respectively. In NMD with closed UA, only T100 reached a high critical leak of 69 L/min. Besides, inspiratory trigger sensitivity change was often necessary to avoid PVA. CONCLUSIONS: Home ventilators were able to avoid PVA in high-level leak conditions. However, asynchrony appeared in cases of abnormal mechanical properties of respiratory system or closed UA. In case of closed UA, the EPAP should be adjusted prior to the inspiratory trigger. TRIAL REGISTRATION: Not applicable.

Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit.

Background: Patient-ventilator asynchrony (PVA) frequently occurs in mechanically ventilated patients within the ICU and has the potential for harm. Depending solely on the health care team cannot accurately and promptly identify PVA. To address this issue, our team has developed a cloud-based platform for monitoring mechanical ventilation (MV), comprising the PVA-RemoteMonitor system and the 24-h MV analysis report. We conducted a survey to evaluate physicians' satisfaction and acceptance of the platform in 14 ICUs. Methods: Data from medical records, clinical information systems, and ventilators were uploaded to the cloud platform and underwent data processing. The data were analyzed to monitor PVA and displayed in the front-end. The 24-h analysis report for MV was generated for clinical reference. Critical care physicians in 14 hospitals' ICUs that involved in the platform participated in a questionnaire survey, among whom 10 physicians were interviewed to investigate physicians' acceptance and opinions of this system. Results: The PVA-RemoteMonitor system exhibited a high level of specificity in detecting flow insufficiency, premature cycle, delayed cycle, reverse trigger, auto trigger, and overshoot, with sensitivities of 90.31 %, 98.76 %, 99.75 %, 99.97 %, 100 %, and 99.69 %, respectively. The 24-h analysis report supplied essential data about PVA and respiratory mechanics. 86.2 % (75/87) of physicians supported the application of this platform. Conclusions: The PVA-RemoteMonitor system accurately identified PVA, and the MV analysis report provided guidance in controlling PVA. Our platform can effectively assist ICU physicians in the management of ventilated patients.

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. METHODS: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. RESULTS: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p< 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p< 0.01). CONCLUSIONS: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.

Clinical evidence and technical aspects of innovative technology and monitoring of chronic NIV in COPD: a narrative review.

INTRODUCTION: Chronic nocturnal noninvasive ventilation (NIV) improves outcomes in COPD patients with chronic hypercapnic respiratory failure. The aim of chronic NIV in COPD is to control chronic hypercapnic respiratory insufficiency and reduce symptoms of nocturnal hypoventilation, thereby improving quality of life. Chronic NIV care is more and more offered exclusively at home, enabling promising outcomes in terms of patient and caregiver satisfaction, hospital care consumption and cost reduction. Yet, to achieve and maintain optimal ventilation, during adaptation and follow-up, effective feasible (home) monitoring poses a significant challenge. AREAS COVERED: Comprehensive monitoring of COPD patients receiving chronic NIV requires integrating data from ventilators and assessment of the patient's status including gas exchange, sleep quality, and patient-reported outcomes. The present article describes the physiological background of monitoring during NIV and aims to provide an overview of existing methods for monitoring, assessing their reliability and clinical relevance. EXPERT OPINION: Patients on chronic NIV are 'ideal' candidates for home monitoring; the advantages of transforming hospital to home care are huge for patients and caregivers and for healthcare systems facing increasing patient numbers. Despite the multitude of available monitoring methods, identifying and characterizing the most relevant parameters associated with optimal patient well-being remains unclear.